Through the concept of network slicing, a single physical network infrastructure can be split into multiple logically-independent Network Slices (NS), each of which is customized for the needs of its respective individual user or industrial vertical. In the beyond 5G (B5G) system, this customization can be done for many targeted services, including, but not limited to, 5G use cases and beyond 5G. The network slices should be optimized and customized to stitch a suitable environment for targeted industrial services and verticals. This paper proposes a novel Quality of Service (QoS) framework that optimizes and customizes the network slices to ensure the service level agreement (SLA) in terms of end-to-end reliability, delay, and bandwidth communication. The proposed framework makes use of network softwarization technologies, including software-defined networking (SDN) and network function virtualization (NFV), to preserve the SLA and ensure elasticity in managing the NS. This paper also mathematically models the end-to-end network by considering three parts: radio access network (RAN), transport network (TN), and core network (CN). The network is modeled in an abstract manner based on these three parts. Finally, we develop a prototype system to implement these algorithms using the open network operating system (ONOS) as a SDN controller. Simulations are conducted using the Mininet simulator. The results show that our QoS framework and the proposed resource allocation algorithms can effectively schedule network resources for various NS types and provide reliable E2E QoS services to end-users.

To sustain and expand the agricultural economy even as its workforce shrinks, the efficiency of farm operations must be improved. One key to efficiency improvement is completely unmanned driving of farm machines, which requires stable monitoring and control of machines from remote sites, a safety system to ensure safe autonomous driving even without manual operations, and precise positioning in not only small farm fields but also wider areas. As possible solutions for those issues, we have developed technologies of wireless network quality prediction, an end-to-end overlay network, machine vision for safety and positioning, network cooperated vehicle control and autonomous tractor control and conducted experiments in actual field environments. Experimental results show that: 1) remote monitoring and control can be seamlessly continued even when connection between the tractor and the remote site needs to be switched across different wireless networks during autonomous driving; 2) the safety of the autonomous driving can automatically be ensured by detecting both the existence of people in front of the unmanned tractor and disturbance of network quality affecting remote monitoring operation; and 3) the unmanned tractor can continue precise autonomous driving even when precise positioning by satellite systems cannot be performed.

In this paper, for improving the robustness of D2D-based SNS by avoiding the cascading failure, we propose an autonomous decentralized friendship management called virtual temporal friendship creation. In our proposed virtual temporal friendship creation, some virtual temporal friendships are created among users based on an optimization problem to improve the robustness although these friendships cannot be used to perform the message exchange in SNS. We investigate the impact of creating a new friendship on the node resilience for the optimization problem. Then we consider an autonomous decentralized algorithm based on the obtained results for the optimization problem of virtual temporal friendship creation. We evaluate the performance of the virtual temporal friendship creation with simulation and investigate the effectiveness of this method by comparing with the performance of a method with meta-heuristic algorithm. From numerical examples, we show that the virtual temporal friendship creation can improve the robustness quickly in an autonomous and decentralized way.

Cloud-based Global Navigation Satellite Systems (CB-GNSS) positioning architecture that offloads part of GNSS positioning computation to cloud/edge infrastructure has been studied as an architecture that adds valued functions via the network. The merits of CB-GNSS positioning are that it can take advantage of the abundant computing resources on the cloud/edge to add unique functions to the positioning calculation and reduce the cost of GNSS receiver terminals. An issue in GNSS positioning is the degradation in positioning accuracy in unideal reception environments where open space is limited and some satellite signals are blocked. To resolve this issue, we propose a satellite selection algorithm that effectively removes the multipath components of blocked satellite signals, which are the main cause of drop in positioning accuracy. We build a Proof of Concept (PoC) test environment of CB-GNSS positioning architecture implementing the proposed satellite selection algorithm and conduct experiments to verify its positioning performance in unideal static and dynamic conditions. For static long-term positioning in a multipath signal reception environment, we found that CB-GNSS positioning with the proposed algorithm enables a low-end GNSS receiver terminal to match the positioning performance comparable to high-end GNSS receiver terminals in terms of the FIX rate. In an autonomous tractor driving experiment on a farm road crossing a windbreak, we succeeded in controlling the tractor's autonomous movement by maintaining highly precise positioning even in the windbreak. These results indicates that the proposed satellite selection algorithm achieves high positioning performance even in poor satellite signal reception environments.

For beyond 5G era, in network function virtualization (NFV) environments, service chaining can be utilized to provide the flexible network infrastructures needed to support the creation of various application services. In this paper, we propose a dynamic service chain construction based on model predictive control (MPC) to utilize network resources. In the proposed method, the number of data packets in the buffer at each node is modeled as a dynamical system for MPC. Then, we formulate an optimization problem with the predicted amount of traffic injecting into each service chain from users for the dynamical system. In the optimization problem, the transmission route of each service chain, the node where each VNF is placed, and the amount of resources for each VNF are determined simultaneously by using MPC so that the amount of resources allocated to VNFs and the number of VNF migrations are minimized. In addition, the performance of data transmission is also controlled by considering the maximum amount of data packets stored in buffers. The performance of the proposed method is evaluated by simulation, and the effectiveness of the proposed method with different parameter values is investigated.

In this paper, a novel method of resource abstraction and an abstracted-resource model for dynamic resource control in optical access networks are proposed. Based on this proposal, an implementation assuming application to 5G mobile fronthaul and backhaul is presented. Finally, an evaluation of the processing time for resource allocation using this method is performed using a software prototype of the control function. From the results of the evaluation, it is confirmed that the proposed method offers better characteristics than former approaches, and is suitable for dynamic resource control in 5G applications.

For recent datacenter networks, RDMA (Remote Direct Memory Access) can ease the overhead of the TCP/IP protocol suite. The RoCEv2 (RDMA over Converged Ethernet version 2) standard enables RDMA on widely deployed Ethernet technology. RoCEv2 leverages priority-based flow control (PFC) for realizing the lossless environment required by RDMA. However, PFC is well-known to have the technical weakness of head-of-line blocking. Congestion control for RDMA is a very hot research topic for datacenter networks. In this paper, we propose a novel congestion control algorithm for RoCEv2, TIDD (Timer-based Increase and Delay-based Decrease). TIDD basically combines the timer-based increase of DCQCN and delay-based decrease of TIMELY. Extensive simulation results show that TIDD satisfies the high throughput and low latency required for datacenter networks.

Heterogeneous drone swarms are large hybrid drone clusters in which multiple drones with different wireless protocols are interconnected by some translator drones called GWs. Nowadays, because inexpensive drones, such as toy drones, have become widely used in society, the technology for constructing huge drone swarms is attracting more and more attention. In this paper, we propose an autonomous GW mobility control algorithm for establishing stabilized and low-delay communication among heterogeneous clusters, assuming that only GWs are controllable and relocatable to ensure the flexible operationality of drone swarms. Our proposed algorithm is composed of two independent sub algorithms - the Link Stabilizer and the Path Optimizer. The Stabilizer maintains the neighbor links and consists of two schemes: the neighbor clustering based on relative velocities and the GW velocity calculation using a kinetic model. The Optimizer creates a shortcut to reduce the end-to-end delay for newly established communication by relocating the GW dynamically. We also propose a conceptual protocol design to implement this algorithm into real-world drone swarms in a distributed manner. Computer simulation reveals that the Stabilizer improved the connection stability for all three mobility models even under the high node mobility, and the Optimizer reduced the communication delay by the optimal shortcut formation under any conditions of the experiments and its performance is comparable to the performance upper limit obtained by the brute-force searching.

High-altitude platform stations (HAPSs) are recognized as a promising technology for coverage extension in the sixth generation (6G) mobile communications and beyond. The purpose of this study is to develop a HAPS system with a coverage radius of 100km and high capacity by focusing on the following two aspects: array antenna structure and user selection. HAPS systems must jointly use massive multiple-input multiple-output (mMIMO) and multiuser MIMO techniques to increase their capacity. However, the coverage achieved by a conventional planar array antenna is limited to a circular area with a radius of only tens of kilometers. A conventional semi-orthogonal user selection (SUS) scheme based on the orthogonality of channel vectors achieves high capacity, but it has high complexity. First, this paper proposes a cylindrical mMIMO system to achieve an ultra-wide coverage radius of 100km and high capacity. Second, this paper presents a novel angle-based user selection (AUS) scheme, where a user selection problem is formulated as a maximization of the minimum angular difference between users over all user groups. Finally, a low-complexity suboptimal algorithm (SA) for AUS is also proposed. Assuming an area with a 100km radius, simulation results demonstrate that the proposed cylindrical mMIMO system improves the signal-to-interference-plus-noise ratio by approx. 12dB at the boundary of the area, and it achieves approx. 1.5 times higher capacity than the conventional mMIMO which uses a planar array antenna. In addition, the results show that the proposed AUS scheme improves the lower percentiles in the system capacity distribution compared with SUS and basic random user selection. Furthermore, the computational complexity of the proposed SA is in the order of only 1/4000 that of SUS.

To reduce the complexity of direct radio frequency (RF) undersampling real-time spectrum monitoring in wireless Internet of Things (IoT) bands (920MHz, 2.4GHz, and 5 GHz bands), a design method of sampling frequencies is proposed in this paper. The Direct RF Undersampling receiver architecture enables the use of ADC with sampling clock lower frequency than receiving RF signal, but it needs RF signal identification signal processing from folded spectrums with multiple sampling clock frequencies. The proposed design method allows fewer sampling frequencies to be used than the conventional design method for continuous frequency range (D.C. to 5GHz-band). The proposed method reduced 2 sampling frequencies in wireless IoT bands case compared with the continuous range. The design result using the proposed method is verified by measurement.

Computer networks tend to be subjected to the proliferation of mobile demands, therefore it poses a great challenge to guarantee the quality of network service. For real-time systems, the QoS performance bound analysis for the complex network topology and background traffic in modern networks is often difficult. Network calculus, nevertheless, converts a complex non-linear network system into an analyzable linear system to accomplish more accurate delay bound analysis. The existing network environment contains complex network resource allocation schemes, and delay bound analysis is generally pessimistic, hence it is essential to modify the analysis model to improve the bound accuracy. In this paper, the main research approach is to obtain the measurement results of an actual network by building a measurement environment and the corresponding theoretical results by network calculus. A comparison between measurement data and theoretical results is made for the purpose of clarifying the scheme of bandwidth scheduling. The measurement results and theoretical analysis results are verified and corrected, in order to propose an accurate per-flow end-to-end delay bound analytic model for a large-scale scheduling network. On this basis, the instructional significance of the analysis results for the engineering construction is discussed.

OpenFlow is a widely adopted implementation of the Software-Defined Networking (SDN) architecture. Since conventional network monitoring systems are unable to cope with OpenFlow networks, researchers have developed various monitoring systems tailored for OpenFlow networks. However, these existing systems either rely on a specific controller framework or an API, both of which are not part of the OpenFlow specification, and thus limit their applicability. This article proposes a transparent and low-overhead monitoring system for OpenFlow networks, referred to as Opimon. Opimon monitors the network topology, switch statistics, and flow tables in an OpenFlow network and visualizes the result through a web interface in real-time. Opimon monitors a network by interposing a proxy between the controller and switches and intercepting every OpenFlow message exchanged. This design allows Opimon to be compatible with any OpenFlow switch or controller. We tested the functionalities of Opimon on a virtual network built using Mininet and a large-scale international OpenFlow testbed (PRAGMA-ENT). Furthermore, we measured the performance overhead incurred by Opimon and demonstrated that the overhead in terms of latency and throughput was less than 3% and 5%, respectively.